class_docstr: The full citation network datasets from the
  `"Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via
  Ranking" <https://arxiv.org/abs/1707.03815>`_ paper.
  Nodes represent documents and edges represent citation links.
  Datasets include :obj:`"Cora"`, :obj:`"Cora_ML"`, :obj:`"CiteSeer"`,
  :obj:`"DBLP"`, :obj:`"PubMed"`.
  
  Args:
      root (str): Root directory where the dataset should be saved.
      name (str): The name of the dataset (:obj:`"Cora"`, :obj:`"Cora_ML"`
          :obj:`"CiteSeer"`, :obj:`"DBLP"`, :obj:`"PubMed"`).
      transform (callable, optional): A function/transform that takes in an
          :obj:`torch_geometric.data.Data` object and returns a transformed
          version. The data object will be transformed before every access.
          (default: :obj:`None`)
      pre_transform (callable, optional): A function/transform that takes in
          an :obj:`torch_geometric.data.Data` object and returns a
          transformed version. The data object will be transformed before
          being saved to disk. (default: :obj:`None`)
      to_undirected (bool, optional): Whether the original graph is
          converted to an undirected one. (default: :obj:`True`)
      force_reload (bool, optional): Whether to re-process the dataset.
          (default: :obj:`False`)
  
  **STATS:**
  
  .. list-table::
      :widths: 10 10 10 10 10
      :header-rows: 1
  
      * - Name
        - #nodes
        - #edges
        - #features
        - #classes
      * - Cora
        - 19,793
        - 126,842
        - 8,710
        - 70
      * - Cora_ML
        - 2,995
        - 16,316
        - 2,879
        - 7
      * - CiteSeer
        - 4,230
        - 10,674
        - 602
        - 6
      * - DBLP
        - 17,716
        - 105,734
        - 1,639
        - 4
      * - PubMed
        - 19,717
        - 88,648
        - 500
        - 3
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code: def process(self) -> None:
      data = read_npz(
          self.raw_paths[0], to_undirected=self.to_undirected
      )
      data = (
          data
          if self.pre_transform is None
          else self.pre_transform(data)
      )
      self.save([data], self.processed_paths[0])
  
file_path: pytorch_geometric/torch_geometric/datasets/citation_full.py
imports: import os.path
  from typing import Callable, Optional
  from torch_geometric.data import InMemoryDataset, download_url
  from torch_geometric.io import read_npz
label_desc: Use image node_id 7 for calling the CitationFull obj's underlying member method code with example usage: obj.process() without return types